Optimized Pattern Partitioning for Multi-Pass Printing: PARAOMASKING

Abstract

In halftone-driven imaging pipelines focus is often placed on halftone pattern design as the main contributor to overall output quality. However, for sequential or cumulative imaging technologies, such as multi-pass printing, an important element is also pattern partitioning – how the overall halftone pattern is divided among the different partial imaging events such as printing passes. Partitioning is usually designed agnostically of the halftone pattern, making it impossible to optimize for the joint effect of halftone and partitioning. However, even a good halftone pattern coupled with a good partitioning scheme does not guarantee well partitioned halftones and can impact image quality attributes. In this paper a novel approach called PARAOMASKING is presented that benefits from the pattern-determinism of PARAWACS halftoning and proposes a partitioning scheme for multi-pass printing such that optimality is also obtained for partitioned halftones. Results – both digital and printed – show how it can lead to significant improvements in partial pattern quality and overall pattern quality. Consequently, output attributes such as grain, coalescence and pattern robustness are improved. The focus here is on blue-noise pattern preservation but the approach can also be extended to other objectives, e.g., maximizing per-pass clustering.